code: def test_transform_3d():
      x1 = torch.randn(100, 17, 3)
      x2 = x1.clone()
      rot_y = torch.randn(100, 1)
      rot_x = torch.randn(100, 1)
      is_reverse = True
      r1 = transform_3d(x1, rot_y, rot_x, is_reverse)
      r2 = Transform3DV1(x2, rot_y, rot_x, is_reverse)
      if (r1 - r2 < 1e-5).all():
          print("Passed.")
      else:
          raise ValueError("At least one computation is not corrected")
  
imports: import os
  import torch
  import torch.optim
  import numpy
  from sklearn.metrics import auc
label_desc: Use image node_id 17 for calling a global function with example usage: test_transform_3d() without return types